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Earley On Podcast Episode 2: Internal Collaboration and the Customer Experience


In this episode, Seth Earley, CEO of Earley Information Science, discusses the topic of how internal collaboration affects customer experience. Seth addresses the issue of complexity, operational silos, workflow and metrics that large organizations face when trying to align their efforts around delivering world class digital experiences.



Hi, my name is Seth Earley and welcome to Earley On, our weekly podcast. I’m the founder and CEO of Earley Information Science, and this week we’re going to talk about why customer experience requires collaboration. And the point here is that anything that we’re doing in terms of external capabilities, customer capabilities, customer facing experience, requires supporting processes that are upstream, that are internal. Alright? Things don’t happen magically. We have people that have to collaborate and create content that is then pushed out and presented for our users. And the quality of those external experiences are really dependent on the quality of internal source systems, in the ability for us to streamline information flows. For example, consider the source of customer support information. Ultimately, that content comes from engineers and experts who design the products and solutions. Making that information consumable in all of these multiple downstream contexts really requires a means of finding, reusing and sharing the content and data, alright? It comes from the heads of engineers and it has to make it into the hands of customers.

And when we want to make this more effective, we have to make our internal collaboration low friction. We have to make it efficient. We have to allow for faster clock speeds of responsiveness, of decision making. And to me, it’s all about this information ecosystem where we have these information flows and ultimately we are making decisions faster and more effectively in order to create our products and services that we’re putting out to our marketplace.

So when you think about this from the perspective of, say, a product experience, so we’re on a website, we’re going to access some information and purchase a product. Again, the product information itself comes from some upstream set of processes. It comes from either product development people or engineering organization, or if it’s a retailer it comes from a procurement and a sourcing department. And the product information itself has to be on boarded, which requires collaboration across multiple groups, multiple individuals. You might require a catalog manager, a merchandiser, or product information management organization, and that product data and that product content and those digital assets have to be assembled for downstream consumption. We have to integrate that with marketing planning. We have to integrate that with promotions, with merchandising calendar, and that requires collaboration across product management and marketing and category managers and merchandisers and so on. We have to try to understand our demographics and our firmagraphics, depending upon whether business to consumer or business to business, and create demand generation that is going to be tracked according to the effectiveness of campaigns, we have to pull that information back into our marketing and our planning process, and we have to coordinate across different departments in order to create store promotions or create our personalization strategies. And all of this actually comes together when we start looking at content operations and content strategies.

So there’s a whole range of different groups and individuals and departments that have to share this information. And what happens is many organizations are dealing with transformations where they’re looking at an external experience that is highly capable, that will require a product or a product suite or technologies that allow them to create this dynamic user experience, but then they don’t have the upstream processes that are actually going to create that content and create those different user experiences that can be then pushed downstream. Or, I’ve heard of enriched product content, so videos or how-to’s and so on, will significantly increase the conversions of a product on a website, something like 50 or 75 percent in some cases. But that content is not well managed. That content gets put up and integrated through acts of heroics, which do not scale.

So we really need to have content operations and content processes that are going to allow us to integrate the product information with the appropriate content, so that we can give people the information they need in the context of their task. In a way, this is all about knowledge processes, right? When we are harvesting knowledge from engineers, we are integrating the knowledge of marketers and merchandisers, and we are putting this into the context of a user experience where a user is able to get to exactly what they need when they need it, when they’re trying to solve a particular problem. So the Holy Grail of knowledge management has always been the right information at the right time for the right person. Well, that’s what contextualization is. That’s what personalization is.

So in order to make all that work, we need to have an architecture that allows us to integrate product information with content and rich assets, and we have to have the operations that will leverage the data and leverage the analytics from user behaviors and user experience and bring that into the process, so we can adjust our promotions and we can adjust our engagement and we can look at our content lifecycle and align that with our customer lifecycle.

So we’re actually creating an infographic that goes through all the departments and processes and people and content that actually has checklists. And the checklists are for various processes and various departments. So for instance, you might have a checklist for supplier onboarding. So have suppliers given us an inventory of available metadata? Why do we need that? Well, we need to know what kind of metadata we can use to power the attributes that will allow customers to make selections and it will allow us to assemble content. Have our product data models been validated against merchandising requirements? So in other words, we have to think about how we describe our product, the isn’t this and about this and the attributes. And those have to be lined up with how those customers are going to look for that product and how the merchandiser is going to present that. So we have to validate that.

We also have to think about quality review, data quality review. Many times, data quality is something that’s dealt with downstream. So in other words, we might get bad data from a vendor, and nobody cares about that until somebody has to put that information into a product information management system or put it up on the website, or they don’t even notice it until it’s on the website. We can deal with that problem at the source if we have data scorecards, quality review processes and scorecards that are in place, and procurement is the organization that needs to be involved with that. So we need to make sure that we define our responsibilities around product data curation, and that has to be part of a master services agreement. So again, those are just some of the checklist items for supplier onboarding. And again, we’re trying to link the performance around data standards on the part of a supplier and link that to our procurement performance measures.

That’s just one checklist. We have product data onboarding checklists. We have marketing data checklists. We have demand generation checklists and Omni-channel experience and digital asset management and content operations and self-service metrics and personalization strategy checklists and analytics and content architecture. So this infographic is in process, and we’ve been working with our designers to include all of this information so that we can look at all the different departments, the different roles, the different players, and the content and the data that gets moved through this process. And when you start looking at it this way you start realizing that creating this really dynamic, personalized user experience is something that takes a lot of internal work.

Now, here’s the rub. Organizations are looking at digital transformations, and they’re thinking of these digital transformations from the perspective of that external user experience. So they’ll go through and they’ll replatform, they’ll look at product data, they’ll look at all of these processes, they’ll try to create this wonderful, innovative user experience. But then what happens? They push it out there, but they don’t have sustainable processes for keeping it up to date and for keeping it fresh and building a dynamic. It’s all built through, hand-crafted, shall we say. I don’t want to say that. I mean, most organizations are dealing with some level of dynamic content. But the majority are not aligning their internal marketing operations with the capabilities of the website. And in fact, we went through one organization where part of the transformation included an employee experience transformation. So it was a customer experience transformation and an employee experience transformation. The customer experience transformation got funded, the employee experience transformation did not. So what does that mean? That means that all of those people who are working upstream and all of those processes who need to collaborate and need to find stuff and need to be able to make quick decisions and reuse their content and reuse their assets and find what they need when they need it, can’t do that. And that is going to impact the downstream systems. That’s going to impact the ability to really create that optimum user experience.

All of these things are connected, right? It’s an ecosystem of information flows. And unless we have those internal systems and processes smoothly running with low friction, meaning I can find my stuff, search is working effectively, I can reuse things, I can get answers that I need, I can produce my output from someone else’s input, and then that output can go downstream to another process, unless we have that capability, we’re really not solving the problem. So when you think about what all organizations are trying to do, they’re all trying to do personalization, they’re all trying to build an Omni-channel experience, whether you’re B2B or B2C. You’re trying to market one to one, you’re trying to engage more effectively with customers. And what is engagement? Engagement is about understanding a problem and a need and solving that and addressing that, and giving people what they need to solve the problem that they’re trying to solve to accomplish their task or their goal.

That’s what people do, right? When you work with somebody who can answer your questions, or you don’t know what you want and they help you through the process, well, that’s that engagement. And we’re trying to do the same thing on the website. We’re trying to do that at scale, right? That’s what websites and web engines and ecommerce and digital capabilities and digital engagement is all evolving to, this process where you can engage just as you would with a person. Well, what is that, now?

That’s starting to bring into the fore the idea of artificial intelligence, right? We just did some white papers and some articles on this topic, and our perspective is you can start to add these elements of AI, machine intelligence, machine learning, to this process incrementally. We have an upcoming roundtable on this topic. We have a lot of materials on the website on this topic, and we’d love to hear what’s working for your organization or what you’re struggling with. But again, ultimately, it’s all about information access. Ultimately it’s all about this dynamic user experience. It’s about upstream processes that are helping us get that information to the customer, and then it’s about overlaying some intelligent systems that can understand more about what the user is doing. There’s a whole range of things that we can include in that bucket.

Next week, I’ll talk a little bit more about some of the flavors of AI that you might not think of as AI. Some things that you’re probably already doing that maybe with a little bit of tweaking, a little bit of adjustment, can actually bring you a step closer to AI, practical artificial intelligence. At any rate, that’s it for this week. My name is Seth Earley, and you can reach me at That’s You can certainly check out the website and I will look forward to speaking with you next time.

Seth Earley
Seth Earley
Seth Earley is the Founder & CEO of Earley Information Science and the author of the award winning book The AI-Powered Enterprise: Harness the Power of Ontologies to Make Your Business Smarter, Faster, and More Profitable. An expert with 20+ years experience in Knowledge Strategy, Data and Information Architecture, Search-based Applications and Information Findability solutions. He has worked with a diverse roster of Fortune 1000 companies helping them to achieve higher levels of operating performance.

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